You have a set of applications running on a Google Kubernetes Engine (GKE) cluster, and you are using Stackdriver Kubernetes Engine Monitoring. You are bringing a new containerized application required by your company into production. This application is written by a third party and cannot be modified or reconfigured. The application writes its log information to /var/log/app_messages.log, and you want to send these log entries to Stackdriver Logging. What should you do?

• A.Write a script to tail the log file within the pod and write entries to standard output. Run the script as a sidecar container with the application's pod. Configure a shared volume between the containers to allow the script to have read access to /var/log in the application container.
• B.Install Kubernetes on Google Compute Engine (GCE> and redeploy your applications. Then customize the built-in Stackdriver Logging configuration to tail the log file in the application's pods and write to Stackdriver Logging.
• C.Deploy a Fluentd daemonset to GKE. Then create a customized input and output configuration to tail the log file in the application's pods and write to Slackdriver Logging.
• D.Use the default Stackdriver Kubernetes Engine Monitoring agent configuration.

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You are deploying an application that needs to access sensitive information. You need to ensure that this information is encrypted and the risk of exposure is minimal if a breach occurs. What should you do?

• A.Store the encryption keys in Cloud Key Management Service (KMS) and rotate the keys frequently
• B.Inject the secret at the time of instance creation via an encrypted configuration management system.
• C.Integrate the application with a Single sign-on (SSO) system and do not expose secrets to the application
• D.Leverage a continuous build pipeline that produces multiple versions of the secret for each instance of the application.

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You support an application running on App Engine. The application is used globally and accessed from various device types. You want to know the number of connections. You are using Stackdriver Monitoring for App Engine. What metric should you use?

• A.(lex/jnstance/connections/current
• B.tcp_ssl_proxy/open_connections
• C.tcp_ssl_proxy/new_connections
• D.flex/connections/current

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You support an application that stores product information in cached memory. For every cache miss, an entry is logged in Stackdriver Logging. You want to visualize how often a cache miss happens over time. What should you do?

• A.Link Stackdriver Logging as a source in Google Data Studio. Filler (he logs on the cache misses.
• B.Create a logs-based metric in Stackdriver Logging and a dashboard for that metric in Stackdriver Monitoring.
• C.Configure Stackdriver Profiler to identify and visualize when the cache misses occur based on the logs.
• D.Configure BigOuery as a sink for Stackdriver Logging. Create a scheduled query to filter the cache miss logs and write them to a separate table

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You manage several production systems that run on Compute Engine in the same Google Cloud Platform (GCP) project. Each system has its own set of dedicated Compute Engine instances. You want to know how must it costs to run each of the systems. What should you do?

• A.In the Google Cloud Platform Console, use the Cost Breakdown section to visualize the costs per system.
• B.Assign all instances a label specific to the system they run. Configure BigQuery billing export and query costs per label.
• C.Enrich all instances with metadata specific to the system they run. Configure Stackdriver Logging to export to BigQuery, and query costs based on the metadata.
• D.Name each virtual machine (VM) after the system it runs. Set up a usage report export to a Cloud Storage bucket. Configure the bucket as a source in BigQuery to query costs based on VM name.

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